控制理论(社会学)
还原(数学)
角位移
跟踪(教育)
观察员(物理)
流离失所(心理学)
机器人
非线性系统
人工神经网络
跟踪误差
职位(财务)
计算机科学
工程类
模拟
人工智能
数学
控制(管理)
物理
心理学
教育学
几何学
财务
量子力学
经济
心理治疗师
作者
Jintao Lei,Zhuangzhuang Wang
摘要
Abstract Background For the fracture reduction robot, the position tracking accuracy and compliance are affected by dynamic loads from muscle stretching, uncertainties in robot dynamics models, and various internal and external disturbances. Methods A control method that integrates a Radial Basis Function Neural Network (RBFNN) with Nonlinear Disturbance Observer is proposed to enhance position tracking accuracy. Additionally, an admittance control is employed for force tracking to enhance the robot's compliance, thereby improving the safety. Results Experiments are conducted on a long bone fracture model with simulated muscle forces and the results demonstrate that the position tracking error is less than ±0.2 mm, the angular displacement error is less than ±0.3°, and the maximum force tracking error is 26.28 N. This result can meet surgery requirements. Conclusions The control method shows promising outcomes in enhancing the safety and accuracy of long bone fracture reduction with robotic assistance.
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